1. Field of the Invention
The present invention relates to an adaptive learning air/fuel ratio control applied to a fuel injection system for an internal combustion engine.
2. Prior Art
Precise air/fuel ratio control is required for maximizing the reduction of hydrocarbon (HC), carbon monoxide (CO) and oxides of nitrogen (NOx) emissions. In order to achieve good air/fuel ratio control, closed-loop fuel control systems employing an exhaust gas oxygen (EGO) sensor to correct any air/fuel ratio errors have been used. Furthermore, adaptive air/fuel control methods have been used to achieve better air/fuel ratio control by reducing the time it takes for the feedback loop to find the set point, i.e., stoichiometry, subsequent to a change in the engine operating point or upon entering closed loop engine control.
In a fuel injection system with an adaptive air/fuel ratio control capability, the actual fuel injection time duration can be obtained by multiplying the basic fuel injection time duration based on the current operating condition by the learning control correction factor.
It is known, for example from U.S. Pat. No. 4,594,985, in an adaptive learning control to obtain the learning control correction factor by initially setting its value at a predetermined initial value. Its value is adjusted according to the mean value of each two successive end values of the air/fuel ratio feedback complementing factor in the constantly increasing and constantly decreasing mode. If the mean value is larger than a predetermined high limit value, the current value of the learning control correction factor is incremented by a predetermined amount. If the mean value is smaller than a predetermined low limit value, the current value of the learning control correction factor is decremented by a predetermined amount. If the mean value is between the high limit and the low limit, the current value of the learning control correction factor is unchanged.
Whether such a system is rich or lean is determined by the mean value of the two successive peak and valley values of the air/fuel ratio feedback complementing factor. If the mean value is greater than an upper limit, the system is considered to be running lean, the learning control correction factor is thus incremented in order to adjust the system towards stoichiometry. If the mean value is less than a lower limit, the system is considered to be running rich, the learning control correction factor is then decremented in order to adjust the system towards the stoichiometry. Also, the increment or decrement amount of the learning control correction factor is a predetermined constant.
Such a system has drawbacks, first, by using only the mean value of two successive end values it is not always possible to correctly determine if the system is running lean or rich. In addition, the mean value of the two successive end values of the air/fuel ratio does not tell how far off the system is operating away from the stoichiometry point. Moreover, by using a fixed increment amount or decrement amount to adjust the learning control correction factor, the adjusting amount may be too small in some situations and too large in other situations.
It is also known to have a system with open loop fuel control wherein the air/fuel ratio is determined by a predetermined table based on the engine operating conditions such as load, engine speed, engine coolant temperature, etc. During system closed-loop fuel operation, the air/fuel ratio is constantly decremented when the EGO sensor indicates lean until a transition from lean to rich is sensed and the air/fuel ratio is constantly incremented when the EGO sensor indicates rich until a transition from rich to lean is sensed. At the time when the EGO sensor senses a transition from rich to lean status, because of the transport delay of the system, the air/fuel ratio is in fact too large. In order to make the air/fuel ratio converge faster towards stoichiometry, the air/fuel ratio is decremented by an amount when the EGO sensor indicates a rich to lean transition. Likewise, the air/fuel ratio is incremented by an amount when the EGO sensor indicates a lean to rich transition.
Accordingly, in the normal closed-loop fuel operation, the interleaved lean cycle and rich cycle continue and the desired normalized air/fuel ratio should stay in the neighborhood of stoichiometry (i.e., 1.0) regardless whether it is in lean cycle or in rich cycle. Thus, the mean value of the air/fuel ratio in one complete lean and rich cycle gives a proper measure of the system's rich or lean status. If the mean value is greater than an upper limit, the system is rich. If it is less than a lower limit, the system is lean. Since the mean value is calculated over the period of a complete lean and rich cycle, this method is more accurate than the method taking the mean value of each two successive end values of the air/fuel ratio. However, this method requires more calculation than the previous method. It requires an accumulator for summing the air/fuel ratio and a counter for summing the number of air/fuel ratio reading over one complete rich and lean cycle. At the end of one complete rich and lean cycle, a divide operation is required to obtain the mean value of the air/fuel ratio.
It would be desirable to improve the adaptive learning control system for air/fuel ratio and remove the drawbacks as described above. In particular it would be desirable to have a system which is easier to implement.